Compression of Synthetic Aperture Radar (SAR) data may require that both magnitude and phase information be preserved. FIG. 1 shows data processing of synthetic aperture radar data according to prior art. Synthetic aperture radar data 102 are typically collected in analog format by an antenna 101 and is converted to digital format through an Analog-to-Digital (A/D) converter 103. The raw, unprocessed data are referred to as Video Phase History (VPH) data 104, and comprise two components: In-phase (I) and Quadrature (Q). Video phase history data 104 having multiple components, such as I and Q, are typically referred as complex SAR data. Complex SAR data are essential for the generation of complex SAR applications products such as interferograms, polarimetry, and coherent change detection, in which a plurality of such images must be processed and compared.
Video phase history data 104 are then passed through a Phase History Processor (PHP) 105 where data 104 are focused in both range (corresponding to a range focusing apparatus 107) and azimuth (corresponding to an azimuth focusing apparatus 109). The output of phase history processor 105 is referred to as Single Look Complex (SLC) data 110. A detection function 111 processes SLC data 110 to form a detected image 112.
Existing complex SAR sensors collect increasingly large amounts of data. Processing the complex data information and generating resultant imagery products may utilize four to eight times the memory storage and bandwidth that is required for the detected data (I&Q). In fact, some studies suggest exponential growth in associated data throughput over the next decade. However, sensors are typically associated with on-board processors that have limited processing and storage capabilities. Moreover, collected data are often transmitted to ground stations over a radio channel having a limited frequency bandwidth. Consequently, collected data may require compression in order to store or transmit collected data within resource capabilities of data collecting apparatus. Also, a SAR compression algorithm should be robust enough to compress both VPH data 104 and SLC SAR data 110, should produce visually near-lossless magnitude image, and should cause minimal degradation in resultant products 112.
Several compression algorithms have been proposed to compress SAR data. However, while such compression algorithms generally work quite well for magnitude imagery, the compression algorithms may not efficiently compress phase information. Moreover, the phase component may be more important in carrying information about a SAR signal than the magnitude component. With SAR data 102, compression algorithms typically do not achieve compression ratios of more than ten to one without significant degradation of the phase information. Because many of the compression algorithms are typically designed for Electro/Optical (EO) imagery, the compression algorithms rely on high local data correlation to achieve good compression results and typically discard phase data prior to compression. Table 1 lists several compression algorithms discussed in the literature and provide a brief description of each.
TABLE 1Popular Alternative SAR Data Compression AlgorithmsCompression AlgorithmDescriptionBlock AdaptiveChoice of onboard data compressionQuantization (BAQ)methods due to simplicity in coding anddecoding hardware. Low compressionratios achieved (<4:1).Vector QuantizationCodebook created assigning a number for a(VQ)sequence of pixels. Awkwardimplementation since considerablecomplexity required in codebookformulation.Block Adaptive VectorConsists of first compressing data withQuantization (BAVQ)BAQ and then following up with VQ.Similar to BAQ.Karhunen-LoeveStatistically optimal transform forTransform (KLT)providing uncorrelated coefficients;however, computational cost is large.Fast Fourier Transform2-D Fast Fourier Transform (FFT)BAQ (FFT-BAQ)performed on raw SAR data. Before rawdata is transformed, dynamic range foreach block is decreased using a BAQ.Uniform SampledEmphasizes phase accuracy of selectedQuantization (USQ)points.Flexible BAQ (FBAQ)Based on minimizing mean square errorbetween original and reconstructed data.Trellis-CodedUnique quantizer optimization design.Quantization (TCQ)Techniques provide superior signal to noiseratio (SNR) performance to BAQ and VQfor SAR.Block Adaptive ScalarBSAQ's adaptive technique provides someQuantization (BSAQ)performance improvement.
Existing optical algorithms are inadequate for compressing complex multi-dimensional data, such as SAR data compression. For example with optical imagery, because of a human eyesight's natural high frequency roll-off, the high frequencies play a less important role than low frequencies. Also, optical imagery has high local correlation and the magnitude component is typically more important than the phase component. However, such characteristics may not be applicable to complex multi-dimensional data. Consequently, a method and apparatus that provides a large degree of compression without a significant degradation of the processed signal are beneficial in advancing the art in storing and transmitting complex multi-dimensional data. Furthermore, the quality of the processed complex multi-dimensional data is not typically visually assessable. Thus, a means for evaluating the effects of compression on the resulting processed signal is beneficial to adjusting and to evaluating the compression process.